A Comparative Study of Modular and Joint Approaches for Speaker-Attributed ASR on Monaural Long-Form Audio. (arXiv:2107.02852v1 [eess.AS])
(2 min)
Speaker-attributed automatic speech recognition (SA-ASR) is a task to
recognize "who spoke what" from multi-talker recordings. An SA-ASR system
usually consists of multiple modules such as speech separation, speaker
diarization and ASR. On the other hand, considering the joint optimization, an
end-to-end (E2E) SA-ASR model has recently been proposed with promising results
on simulation data. In this paper, we present our recent study on the
comparison of such modular and joint approaches towards SA-ASR on real monaural
recordings. We develop state-of-the-art SA-ASR systems for both modular and
joint approaches by leveraging large-scale training data, including 75 thousand
hours of ASR training data and the VoxCeleb corpus for speaker representation
learning. We also propose a new pipeline that performs the E2E SA-ASR model
after speaker clustering. Our evaluation on the AMI meeting corpus reveals that
after fine-tuning with a small real data, the joint system performs 9.2--29.4%
better in accuracy compared to the best modular system while the modular system
performs better before such fine-tuning. We also conduct various error analyses
to show the remaining issues for the monaural SA-ASR.